Description 
1 online resource (xv, 258 pages) : illustrations (some color) 
Series 
Use R! 

Use R!

Contents 
Introduction  What are compositional data?  Getting started with R  References  Fundamental concepts of compositional data analysis  A practical view to compositional concepts  Principles of compositional analysis  Elementary compositional graphics  Multivariate scales  The Aitchison simplex  References  Distributions for random compositions  Continuous distribution models  Models for count compositions  Relations between distributions  References  Descriptive analysis of compositional data  Descriptive statistics  Exploring marginals  Exploring projections  References  Linear models for compositions  Introduction  Compositions as independent variables  Compositions as dependent variables  Compositions as both dependent and independent variables  Advanced considerations  References  Multivariate statistics  Principal component analysis: exploring codependence  Cluster analysis: detecting natural groups  Discriminant analysis  Other multivariate techniques  References  Zeros, missings, and outliers  Descriptive analysis with and of missings  Working with missing values  Outliers  Descriptive analysis of outliers  Working with outliers  References 
Summary 
This book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in descriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package "compositions," it is also a general introductory text on Compositional Data Analysis. Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software. The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics. Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is selfcontained 
Analysis 
Statistics 

Mathematical statistics 

Statistical Theory and Methods 

Statistics and Computing/Statistics Programs 

Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences 
Bibliography 
Includes bibliographical references and index 
Notes 
Online resource; title from PDF title page (SpringerLink, viewed July 17, 2013) 
Subject 
Mathematical statistics  Data processing.


R (Computer program language)

Form 
Electronic book

Author 
TolosanaDelgado, Raimon.

ISBN 
3642368093 (electronic bk.) 

9783642368097 (electronic bk.) 
